知识不确定性估计可靠的临床决策支持:一个狂妄症风险预后案例研究
Adrian Lindenmeyer1, Sai Veeranki2,3, Stefan Franke1
1Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
Studies in health technology and informatics
|April 24, 2025
概括
光谱规范化神经高斯过程 (SNGP) 和集体神经网络 (ENN) 估计知识不确定性,以提高对人工智能的信任,用于妄想风险预测. 与ENN相比,SNGP在检测不熟悉的数据方面表现优异.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床决策支持
- 医学AI中的不确定性量化
背景情况:
- 预测模型的临床采用受到缺乏信任的限制,特别是当模型遇到不熟悉的数据时.
- 估计知识不确定性 (KU) 对于提高AI在医疗保健环境中的可靠性至关重要.
- 这项研究侧重于妄想风险预测,这是改善患者结果的关键领域.
研究的目的:
- 评估集合神经网络 (ENN) 和光谱规范神经高斯过程 (SNGP) 用于量化知识不确定性 (KU).
- 评估ENN和SNGP在预测妄想风险时检测分布外 (OoD) 数据的能力.
- 将ENN和SNGP的性能与随机森林 (RF) 基线进行比较.
主要方法:
- 一组住院患者被用来训练和测试预测模型.
- 使用ENN,SNGP和随机森林 (RF) 基线预测狂妄症风险.
- 分布外 (OoD) 数据是合成生成的,使用特征随机化和交换技术来测试模型的稳定性.
主要成果:
- 无论是ENN还是SNGP都在妄想风险预测方面取得了高绩效 (AUROC ~ 0.90),与RF基线相当.
- SNGP在检测分布之外 (OoD) 数据方面表现出卓越的表现,在不同的场景中正确识别了82.4%和92.2%的OoD样本.
- 此外,ENN也显示了比基线更好的OOD检测,识别了68.8%和86.0%的OOD样本.
结论:
- 所有评估的模型都有效预测了妄想风险.
- 光谱规范神经高斯过程 (SNGP) 在识别分布外数据方面表现出卓越的能力.
- SNGP强大的知识不确定性估计具有显著的潜力,可以提高临床决策支持系统的可靠性.
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